lecture 22 - Department of Informatics · Actual conference due date: 2016 ... Holland O. &...

[email protected] http://informatics.indiana.edu/rocha/i-bic

biologically Inspired

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Informatics luis rocha 2015

biologically-inspired computing lecture 22


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course outlook

Assignments: 35% Students will complete 4/5 assignments based on algorithms

presented in class Lab meets in I1 (West) 109 on Lab Wednesdays

Lab 0 : January 14th (completed)

Introduction to Python (No Assignment) Lab 1 : January 28th

Measuring Information (Assignment 1) Graded

Lab 2 : February 11th

L-Systems (Assignment 2) Graded

Lab 3: March 25th Cellular Automata & Boolean Networks (Assignment 3) Graded

Lab 4: April 8th Genetic Algorithms (Assignment 4)

Being graded Lab 5: April 22nd

Ant Clustering Algorithm (Assignment 5) Due May 4th

Sections I485/H400


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Readings until now

Class Book Nunes de Castro, Leandro [2006]. Fundamentals of Natural

Computing: Basic Concepts, Algorithms, and Applications. Chapman & Hall. Chapters 1,2,3,7,8 Chapter 5, all sections Section 7.7, 8.3.1,8.3.6,8.3.8-10

Lecture notes Chapter 1: “What is Life?” Chapter 2: “The Logical Mechanisms of Life” Chapter 3: “Formalizing and Modeling the World” Chapter 4: “Self-Organization and Emergent Complex

Behavior” Chapter 5: “Reality is Stranger than Fiction”

posted online @ http://informatics.indiana.edu/rocha/i-bic


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final project schedule

Projects Due by May 6th in Oncourse

ALIFE 15 (14) Actual conference due date: 2016 http://blogs.cornell.edu/alife14nyc/

8 pages (LNCS proceedings format) http://www.springer.com/computer/lncs?SGWI

D=0-164-6-793341-0 Preliminary ideas overdue!

Individual or group With very definite tasks assigned per

member of group

ALIFE 15

http://blogs.cornell.edu/alife14nyc/

http://www.springer.com/computer/lncs?SGWID=0-164-6-793341-0

http://www.springer.com/computer/lncs?SGWID=0-164-6-793341-0



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biological, social and complexity explanations differences and explanations

Emergent behavior Intricate structures and behavior from the interaction of

many simple agents or rules Examples

Cellular Automata, Ant colonies, development, morphogenesis, brains, immune systems, economic markets

Mechanism Parallelism, multiplicity, stigmergy, multi-solutions,

redundancy Design causes

Natural selection, self-organization, epigenetics, culture


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ant clustering algorithm (ACA)

Very simple rules for colony clean up Pick dead ant. if a dead ant is found pick it up (with probability

inversely proportional to the quantity of dead ants in vicinity) and wander. Drop dead ant. If dead ants are found, drop ant (with probability

proportional to the quantity of dead ants in vicinity) and wander.

based on dead body cleaning

x1 x2 x3 … xn-1 xn

Data vector: X

x1 x2 x3 … xn-1 xn

x1 x2 x3 … xn-1 xn

… …

Cluster data (N samples) according to ant clean up rules

Lumer, E. D. and Faieta, B. 1994. Diversity and adaptation in populations of clustering ants. In From Animals To Animats 3, pp. 501-508.


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Group n-dimensional data samples in 2-dimensional grid

x1,1 x1,2 x1,3 … x1,n-1 x1,n

for multivariate data

Data vector: X1 Distance between two data samples

(in original space):

Data vector: X2

( ) ( )∑=

−=n

kkjkiji xxD

1

2,,,xx

e.g. Euclidean

Ants see data points in a certain neighborhood

s2: area of neighborhood (side s, radius 1)

x2,1 x2,2 x2,3 … x2,n-1 x2,n



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ant clustering algorithm (ACA) using thresholds

Clustering rules Pick data sample

If there are few similar Drop data sample.

If there are many similar

( ) ( )

2

1

1

+

=i

ip fkkp

xx

Probability of picking up

Probability of dropping

( )( )

0 if

otherwise 0

,11

)(2 >

−

= ∑×∈ f

Dsf ssNeigh

ji

i jx

xxx α

Discrimination factor

Neighborhood Similarity or density measure

Reduces dimensionality No a priori number of clusters Overshoots number of clusters

( ) ( )( )

2

2

+

=i

iid fk

fpx

xx

( ) ( ) ( ) <

=otherwise1

if2 2kffp ii

id

xxx

Threshold

Improved with Different moving speeds, Short-

term memory, Behavioral switches

Cooling cycle for thresholds, progressive vision, pheromone reinforcement


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1. Project high-dimensional data items onto 2-dimensional grid randomly

2. Distribute N ants randomly on grid 3. repeat

For every ant i in colony Compute neighborhood density f(xi) If ant i is unloaded and its cell is

occupied with data item xi then pick up xi with probability pp(xi)

Else if ant i is loaded with xi and its cell is empty drop xi with probability pd (xi)

Move randomly to neighbor cell with no ant

4. Until maximum iterations

The workings



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sorting with ants Inspired by brood sorting

Same principle as Clustering Rules

Pick data sample of type t If there are few of type t

Drop data sample of type t. If there are many of type t

( ) ( )

2

1

1|

+

=it

ip fkktp

xx

Probability of picking up item of type t

Probability of dropping item of type t

( )( )

0 if

otherwise 0

,11

)(2 >

−

= ∑×∈ f

Dsf ssNeigh

ji

it tjx

xxx α

Neighborhood density of type t

( ) ( )( )

2

2

|

+

=it

itid fk

ftpx

xx



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sorting swarm-robots based on ant algorithm

Holland O. & Melhuish C. (1999) “Stigmergy, Self-organisation, and Sorting in Collective Robotics” Journal of Adaptive Behaviour . 5(2).

Bristol Robotics Laboratory.

See Also: J. L. Deneubourg, S. Goss, N. Franks, A. Sendova-Franks, C. Detrain, L. Chretien. “The Dynamics of Collective Sorting Robot-Like Ants and Ant-Like Robots”. From Animals to Animats: Proc. of the 1st Int. Conf. on Simulation of Adaptive Behaviour. 356-363 (1990).


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bio-inspired collective robotics

C. Ronald Kube, Chris A. Parker, Tao Wang and Hong Zhang. "Biologically Inspired Collective Robotics," Chapter 15 in Recent Developments in Biologically Inspired Computing, de Castro, Leandro N. and Von Zuben, Fernando J., editors, Idea Group Publlishing, 456 pages, 2005.

Box pushing tasks Taxis-based action (reflex

translation or rotation in response to stimulus) and kinesthetic-based action (or proprioception)

+ realignment and repositioning



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bee’s nets natural organization


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self-assembly

Self-assembly algorithm Agents move randomly on a

3D grid of sites. An agent deposits a brick

every time it finds a stimulating configuration. Rule table contains all such

configurations A rule table defines a

particular self-assembly algorithm.

Rule space is very large

From E. Bonabeau. “Swarm Intelligence”.

by stigmergy



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robotic self-assembly “space-station” by dynamic concepts (dynamic-concepts.com)

Phase 1: Simulating construction rules



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robotic self-assembly by dynamic concepts (phase two)

Phase 2: prototype robots



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swarm cognition and art Vitorino Ramos: Pheromone Fields as Swarm Cognitive Maps

Artificial Ants in Digital Image Habitats “A strange Metamorphosis” [From Kafka 2 Red Ant]

ePostCard: V.Ramos CVRM-IST [http://alfa.ist.utl.pt/~cvrm/staff/vramos]; June 2001. Created with an Artificial Ant Colony, that uses images as Habitats, being sensible to their gray levels. At the second row, “Kafka” is replaced as a substrate, by “Red Ant”. In black, the higher levels of pheromone (a chemical evaporative sugar substance used by swarms on their orientation trought out the trails). It’s exactly this artificial evaporation and the computational ant collective group sinergy realocating their upgrades of pheromone at interesting places, that allows for the emergence of adaptation and “perception” of new images. Only some of the 6000 iterations processed are represented. The system does not have any type of hierarchy, and ants communicate only in indirect forms, through out the sucessive alteration that they found on the Habitat.



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swarm art Leonel Moura


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Leonel Moura’s RAP (Robotic Action Painter)

sensors to avoid obstacles, to perceive the

presence of visitors near the case, to check the paper, and most important to detect color.

Two modes Random until color threshold is

detected. Random sketching Random seed from relative direction

measured by an onboard compass. Reactive After passing color

threshold Does not go back Draws only where color exceeds

threshold. Stopping criteria

Pattern in color sensor grid signs off at the corner and flashes

lights

@ The American Museum of Natural History


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Modeling traffic and human group behavior

Vehicles and people modeled as particles in a fluid medium Free traffic: behaves as a gas

Particles move freely Congested traffic: behaves as a liquid

movement of particles strongly depends on surrounding dynamics Shock waves

emerge from density variations Example in congested traffic

The velocity change of a vehicle propagates (with a homogenous time delay) in the opposite direction of traffic as downstream vehicle respond to changes in upstream vehicles

propagation speed aprox. -15 km/h (In free traffic = free vehicle velocity).

Dirk Helbing’s Group

D. Helbing: Traffic and related self-driven many-particle systems. Reviews of Modern Physics 73, 1067-1141 (2003).


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Modeling crowd disasters

People modeled as self-driven many-particle systems Testing individualistic vs herding behavior as well as

environmental solutions

Dirk Helbing’s Group

D. Helbing, A. Johansson and H. Z. Al-Abideen (2007) The Dynamics of Crowd Disasters: An Empirical Study. Physical Review E 75, 046109.


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Natural design principles

self-similar structures Trees, plants, clouds, mountains

morphogenesis Mechanism

Iteration, recursion, feedback Unpredictability

From limited knowledge or inherent in nature? Mechanism

Chaos, measurement Emergence, and self-organization

Complex behavior from collectives of many simple units or agents Cellular Automata, development, morphogenesis, brains

Mechanism Parallelism, multiplicity, multi-solutions, redundancy

(open-ended) Evolution Adaptation, novelty, creativity, learning Mechanism

Reproduction, transmission, variation, selection Collective behavior, network causality

Behavior derived from many inseparable sources Environment, ant colonies, embodiment, epigenetics, culture, immune

systems, economic markets Mechanism

Interactivity, stigmergy, non-holonomic constraints

exploring similarities across nature


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Next lectures

Class Book Nunes de Castro, Leandro [2006]. Fundamentals of Natural

Computing: Basic Concepts, Algorithms, and Applications. Chapman & Hall. Chapter 6

Lecture notes Chapter 1: “What is Life?” Chapter 2: “The logical Mechanisms of Life” Chapter 3: Formalizing and Modeling the World Chapter 4: “Self-Organization and Emergent

Complex Behavior” Chapter 5: “Reality is Stranger than Fiction” Chapter 6: “Von Neumann and Natural Selection” Chapter 7: “Modeling Evolution: Evolutionary

Computation” posted online @ http://informatics.indiana.edu/rocha/i-

bic

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